from torch import nn as nn
import torch
from d2l import torch as d2l

# VGG块的组成：
# 1. 带填充以保持分辨率的卷积层
# 2. 非线性激活函数，如ReLU
# 3. 汇聚层，如最大汇聚层


def vgg_block(num_convs, in_channels, out_channels):
    layers = []
    for _ in range(num_convs):
        layers.append(nn.Conv2d(
            in_channels, out_channels, kernel_size=3, padding=1
        ))
        layers.append(nn.ReLU())
        in_channels = out_channels
    layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
    return nn.Sequential(*layers)


conv_arch = ((1, 64), (1, 128), (2, 256), (2, 512), (2, 512))

def vgg(conv_arch):
    conv_blks = []
    in_channels = 1

    # 卷积层部分
    for (num_convs, out_channels) in conv_arch:
        conv_blks.append(vgg_block(num_convs, in_channels, out_channels))
        in_channels = out_channels

    return nn.Sequential(
        *conv_blks, nn.Flatten(),
        # 全连接层部分
        nn.Linear(out_channels * 7 * 7, 4096), nn.ReLU(), nn.Dropout(0.5),
        nn.Linear(4096, 4096), nn.ReLU(), nn.Dropout(0.5),
        nn.Linear(4096, 10))


net = vgg(conv_arch)

if __name__ == "__main__":
    print("模型结构为：")
    X = torch.randn(size=(1, 1, 224, 224))
    for blk in net:
        X = blk(X)
        print("该层是：", blk.__class__.__name__, 'output shape:\t', X.shape)

    print("\n模型训练：")
    lr, num_epochs, batch_size = 0.05, 10, 128
    train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size, resize=224)
    d2l.train_ch6(net, train_iter, test_iter, num_epochs, lr, d2l.try_gpu())
